My research lies at the intersection of machine learning and cognitive science where I've been interested in developing computational models of higher-level cognition, including categorization, language usage, and memory search. I've explored a number of different questions and research topics to better understand these aspects of higher-level cognition, as described below.

Cognition and computer vision: Recent advances in AI/ML/CV have produced models of image classification and recognition with above-human performance -- but what does that really mean? I've been interested in exploring how these models differ from people, particularly with the way people reason differently about categories depending on context. Some relevant papers:

• J.C. Peterson, J.T. Abbott, and T.L. Griffiths. (2018). Evaluating (and improving) the correspondence between deep neural networks and human representations. Cognitive Science, 42(8), 2648-2669.

• T.L. Griffiths, J.T. Abbott, and A.S. Hsu. (2016). Exploring human cognition using large image databases. Topics in Cognitive Science, 8(3), 569-588.

• Y. Jia, J.T. Abbott, J.L. Austerweil, T.L. Griffiths and T. Darrell. (2013). Visual concept learning: combining machine vision and Bayesian generalization on concept hierarchies. Advances in Neural Information Processing Systems 26.

• J.T. Abbott, K.A. Heller, Z. Ghahramani, and T.L. Griffiths. (2011). Testing a Bayesian measure of representativeness using a large image database. Advances in Neural Information Processing Systems 24.

Color language and cognition across cultures: Questions about color language and cognition have been a central debate in cognitive science. I've been involved in a research program that explores how general computational and statistical principles can illuminate fundamental aspects of color naming across the diverse languages of the world. Some relevant papers:

• A.E. Skelton, G. Catchpole, J.T. Abbott, J.M. Bosten, and A. Franklin. (2017). Biological origins of color categorization. Proceedings of the National Academy of Sciences, 114(21), 5545-5550.

• J.T. Abbott, T.L. Griffiths, and T. Regier. (2016). Focal colors across languages are representative members of colors categories. Proceedings of the National Academy of Sciences, 113(40), 11178-11183.

Semantic memory search: Human memory has a vast capacity, storing all the semantic knowledge, facts, and experiences that people accrue over a lifetime. Given this huge repository of data, retrieving any one piece of information from memory is a challenging computational problem. I've explored how some simple statistical models like random walks and MCMC can reproduce complex human behavior in memory search tasks.

• J.T. Abbott, J.L. Austerweil, and T.L. Griffiths. (2015). Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122(3), 558-569.

• D.D. Bourgin, J.T. Abbott, K.A. Smith, E. Vul, and T.L. Griffiths. (2014). Empirical evidence for Markov chain Monte Carlo in memory search. Proceedings of the 36th Annual Conference of the Cognitive Science Society.

Approximations to Bayesian inference: Probabilistic models of cognition have become a leading framework for exploring questions of higher-level cognition, particularly on how people reason under uncertainty. However, the difficult computations over structured representations that are often required by these models seem incompatible with the continuous and distributed nature of human minds. I've been interested in showing how difficult Bayesian computations can be approximated by more tractable methods which also characterize human behavior.

• J.T. Abbott, J.B. Hamrick, and T.L. Griffiths. (2013). Approximating Bayesian inference with a sparse distributed memory system. Proceedings of the 35th Annual Conference of the Cognitive Science Society.

• J.T. Abbott and T.L. Griffiths. (2011). Exploring the influence of particle filter parameters on order effects in causal learning. Proceedings of the 33rd Annual Conference of the Cognitive Science Society.